class_docstr: Computes Precision, Recall and F1 with respect to a given ``positive_label``.
  For example, for a BIO tagging scheme, you would pass the classification index of
  the tag you are interested in, resulting in the Precision, Recall and F1 score being
  calculated for this tag only.
docstr: Returns
  -------
  A tuple of the following metrics based on the accumulated count statistics:
  precision : float
  recall : float
  f1-measure : float
docstr_tok: ["Returns","--","--","--","-","A","tuple","of","the","following","metrics","based","on","the","accumulated","count","statistics",":","precision",":","float","recall",":","float","f1-measure",":","float"]
code: def get_metric(self, reset=False):
      """
      Returns
      -------
      A tuple of the following metrics based on the accumulated count statistics:
      precision : float
      recall : float
      f1-measure : float
      """
      precision = float(self._true_positives) / float(
          self._true_positives + self._false_positives + 1e-13
      )
      recall = float(self._true_positives) / float(
          self._true_positives + self._false_negatives + 1e-13
      )
      f1_measure = 2.0 * (
          (precision * recall) / (precision + recall + 1e-13)
      )
      if reset:
          self.reset()
      return precision, recall, f1_measure
  
file_path: magnitude/pymagnitude/third_party/allennlp/training/metrics/f1_measure.py
imports: from __future__ import division
  from __future__ import absolute_import
  import torch
  from allennlp.training.metrics.metric import Metric
  from allennlp.common.checks import ConfigurationError
label_desc: Use image node_id 3 for calling the F1Measure obj's underlying member method code with example usage: obj.get_metric(reset) and returns: precision,  recall,  f1_measure